Hey, this is Lance. I want to talk about continual learning with agents in particular showing some examples with deep agents. So, Le has a really nice post on this theme of continual learning in token space and it makes the argument that a big gap between AI agents and humans as we know is ability to learn.Humans continually learn and improve over time. Agents knowledge is typically fixed and doesn't have the same adaptive capability. Now, there's different ways to teach AI systems to learn.So one is learni ...

Product Overview - Langchain introduces a no-code agent builder, aiming to empower non-technical users to create agents easily [2][4] - The agent builder is built upon the "deep agents" architecture, simplifying agent creation to a configuration of tools and prompts [5][11] - The platform supports both chat-based interaction and autonomous background operation via triggers [27] Key Features and Technologies - Deep agents architecture utilizes sub-agents for handling long-running or context-intensive tasks, improving efficiency [5][35] - The platform incorporates a natural language interface for agent creation, abstracting away the complexities of prompt engineering [14][50] - Human-in-the-loop controls, such as interrupts, allow users to review and approve actions before execution, balancing autonomy with oversight [39][40] User Experience and Iteration - The platform provides a chat interface for testing and iterating on agents, allowing users to understand agent behavior and refine instructions [17][18] - An agent inbox facilitates the management of agent conversations and interrupted actions, mirroring a familiar email experience [41][42] - The platform allows users to iterate on agents by updating the agent over time [17] Integration and Deployment - Agents built in the agent builder are compatible with Langraph, enabling seamless transition to production deployments [45] - The platform currently hosts deep agents in the cloud, with plans to allow users to bring their own deep agents and graph architectures [46][47] Future Development and Feedback - Langchain seeks user feedback on optimizing agent improvement workflows, exploring various methods such as chatbot agents, canvas experiences, and thumbs up/down feedback [56][57] - The company is interested in user input on desired tools and triggers, as well as the experience for core platform teams to add new modules [55]